1. Technical Field
The disclosed technology relates to image processing. More specifically, the disclosed technology relates to sidelobe suppression and pixel reduction in image processing.
2. Brief Description of the Related Art
Image processing of received antenna data (such as from a synthetic aperture radar) can be very computationally intensive. Some of this computation is directed to forming the image (converting analog antenna receiver data into digital image data, or pixels) while some is directed to interpreting the image. Converting the antenna data, for example, often involves Fourier transforms of the data, which introduces sidelobes (that interfere with the interpreting of the mainlobe, that is, the desired signal). On the other hand, interpreting the image often involves analyzing the pixels, whose analysis can grow as the square (or higher exponentiation) of the number of pixels, as well as the contrast and resolution of the pixels. Consequently, reducing sidelobes and the number of pixels while maintaining or improving contrast and resolution can enhance such image processing.
Synthetic aperture radar (SAR) is a type of imaging system that usually involves a moving platform (such as an aircraft or satellite) along with an antenna of relatively small aperture that is capable of continuously transmitting and receiving microwave beams of electromagnetic radiation, and a processing system (for example, a computer) to process the received signals. By scanning a region of interest continuously while moving, the SAR system is able to increase the effective aperture of its antenna by acquiring numerous signals of the same region of interest from different antenna locations. This leads to far better microwave imaging resolution than would be possible with a fixed antenna. However, the significant processing requirements (for example, normalizing the image to correct for things like curvature and Doppler effect) have kept SAR technology from being fully exploited until the capabilities of modern computing systems.
The SAR system works by synthetically increasing the aperture size of the antenna. By retaining both phase and magnitude of the backscattered echo signals, the SAR system can synthesize an antenna aperture of very long size, which leads to the improved resolution. This requires a significant amount of post-processing of the data, typically done with a digital computer.
Processing the image data can involve Fourier transforms, such as a discrete Fourier transform (DFT) or fast Fourier transform (FFT), of the data, but this can also introduce sidelobes (noise) to the mainlobe (data of interest). Sidelobes serve to obscure the target signal data (mainlobe) of interest. One way to reduce sidelobes is to introduce weighting of the signal magnitudes to lessen their impact, as is well known in the art (for example, Taylor weighting).
FIG. 5 is a depiction of the sidelobe phenomenon, in this case for a typical unweighted array. As can be seen in FIG. 5, there is a main beam of interest (that provides the desired signal data), but it creates noise in the form of sidelobes. The vertical axis represents relative signal strength in decibels (dB). Sidelobes from other nearby targets (with strong mainlobe signals) can obscure the weaker mainlobe data of targets of interest. For instance, the first sidelobe is only about 13 dB weaker than the mainlobe (see FIG. 5), while the second sidelobe is roughly 18 dB weaker.
Apodization is a form of weighting the image data to suppress the sidelobes, namely by taking the minimum envelope from several amplitude weightings. This enhances the processed image data. Spatially invariant reduction of sidelobes (e.g., Taylor weighting) has the drawback of widening the mainlobe, which reduces resolution. Spatially variant apodization (SVA) can reduce sidelobes without affecting the mainlobe. SVA is used, for example, in SAR image processing as a way to reduce sidelobes while maintaining resolution. SVA attempts to produce an optimal weighting for sidelobe reduction. SVA is spatially variant in that it tunes to local area signal relationships. SVA algorithms make use of phase relationships to suppress sidelobes in a manner that varies with the local neighborhood and is optimal in some sense.
Spatially variant apodization (SVA) is a digital image processing technique for suppressing sidelobes produced by Fourier transform of finite data sequences without affecting the mainlobe width. Dellaire et al., U.S. Pat. No. 5,349,359, entitled “Spatially Variant Apodization,” the entire content of which is incorporated herein by reference. For example, these finite data sequences could represent SAR image data. This process allows each sample or pixel in an image to receive its own frequency domain aperture amplitude weighting function from an infinite number of possible weighting functions. Id.
SVA solutions may be described in terms of an “aperture” filling ratio. Image data may be represented, for example, as a two-dimensional array of complex numbers. Here, “aperture” does not refer to a radar antenna aperture, but instead refers to the size (number of entries) of the image data matrix prior to processing with the two-dimensional Fourier transform to form the image. See FIG. 6, which depicts a two-dimensional array of image data 60 prior to Fourier transform processing. The original (non-zero) data is in the aperture 62, while the zero-filled portion 64 surrounds the aperture 62. Typically, a synthetic aperture radar (SAR) flies through the sky, filling the array aperture with data in one-dimension.
Previous SVA solutions depend on an integer aperture filling ratio (e.g., 1, 1/2, 1/3, etc.), where the integer represents the amount of oversampling. This is the ratio of the non-zero data in the aperture to the full (i.e., Fourier transform) size including zero filling. Generally, the more zero-filled the matrix is prior to Fourier transform processing, the more interpolated the processed image ends up being. The unit ratio is seldom used in SAR processing; wrap-around and edge effects need to be managed, and users prefer a more highly oversampled image. The 1/2 ratio is most commonly used, however it results in nearly 2:1 oversampling of the image, and is wasteful of pixel real-estate, automatic target recognition (ATR) or other image processing, and memory loading. Higher integer ratios (e.g., 1/3, 1/4) only compound the oversampling phenomenon.
Thus, there is a need for an SVA image processing solution that minimizes oversampling yet manages wrap-around and edge effects.